gru-ai: Skill for Claude Code

.claude/skills/healthcheck/SKILL.md

healthcheck is a skill for Claude Code from andrew-yangy/gru-ai. It costs 40 tokens per session (2,196 once invoked), scanned B, original, MIT.

An internal maintenance check in which technical and operations reviewers inspect a codebase and the way the organisation runs. It is scheduled every two weeks and classifies findings by risk.

In plain words
What is it for?
Reviewing codebase health, checking operational health, triaging findings, applying low-risk fixes, and queuing larger issues for later decisions.
Why use it?
It helps catch internal technical and operational problems before they become larger issues, while separating routine maintenance from strategy work.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths.

This is andrew-yangy/gru-ai's own configuration. It tells Claude Code how to work on gru-ai itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gru-ai configures →

Reuse

Borrowing it

Nothing to install: this file belongs to andrew-yangy/gru-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/andrew-yangy/gru-ai/main/.claude/skills/healthcheck/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/andrew-yangy/gru-ai

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for healthcheck

README.md
[![agentmods](https://agentmods.dev/badge/skills/andrew-yangy/gru-ai/healthcheck/github.svg)](https://agentmods.dev/skills/andrew-yangy/gru-ai/healthcheck)
Your own site
<a href="https://agentmods.dev/skills/andrew-yangy/gru-ai/healthcheck"><img src="https://agentmods.dev/badge/skills/andrew-yangy/gru-ai/healthcheck/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for healthcheck

Your own site · 80×15
<a href="https://agentmods.dev/skills/andrew-yangy/gru-ai/healthcheck"><img src="https://agentmods.dev/badge/skills/andrew-yangy/gru-ai/healthcheck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,196 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.02196
Opus 5 $0.00020 $0.01098
Sonnet 5 $0.00008 $0.00439
Haiku 4.5 $0.00004 $0.00220

Measured 13d ago against content hash f67d87e29898, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade B, and why

healthcheck scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Asks the agent to reveal its instructionsmediumSystem prompt leakage

Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.

{JSON output instructions below}
.claude/skills/healthcheck/SKILL.md · 217 lines

How it starts

The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Healthcheck — Internal Maintenance

Role Resolution

Read .claude/agent-registry.json to map roles to agent names. Use each agent's id as the subagent_type when spawning. The CTO handles technical health; the COO handles operational health.


Run a healthcheck: the CTO scans codebase health, the COO checks operational health. Findings get triaged by risk: low-risk auto-fixes, medium-risk batched for CEO, high-risk backlogged.

This is maintenance, not strategy. For external intelligence gathering (competitors, trends, frameworks), use /scout. Healthcheck is the janitor, not the executive.

Step 1: Read Context

Read these before spawning agents:

  • .context/vision.md — guardrails (what NOT to break)
  • .context/preferences.md — CEO standing orders
  • .context/directives/*/directive.json — current directives (to check for staleness)
  • .context/lessons/orchestration.md
  • .context/backlog.json — what's already queued
  • Recent directive reports in .context/reports/ — what was recently done

Step 2: Spawn Healthcheck Agents (Parallel)

Spawn 2 agents in parallel: the CTO (technical) and the COO (operational).

Each agent receives:

  • Their full personality from .claude/agents/{name}.md
  • .context/vision.md (guardrails are critical)
  • .context/preferences.md
  • .context/directives/*/directive.json
  • .context/backlog.json summary
  • Recent directive report summaries (filenames + dates)

Both agents: subagent_type: "general-purpose", model: "opus"

CTO — Technical Health

You are the CTO. You are running a standing healthcheck of the codebase.

Your job: scan the codebase and infrastructure for internal issues.

CHECK THESE AREAS:
1. **Security**: Run `npm audit` in each app directory. Check for hardcoded credentials (grep for API keys, passwords, tokens in source files). Look for unauthed endpoints, injection vectors.
2. **Dependencies**: Check package.json files for outdated or deprecated packages. Look for packages with known CVEs.
3. **Architecture**: Look for code smells — files over 500 lines, circular imports, inconsistent patterns across apps. Check for dead code (unused exports, unreferenced files).
4. **Type safety**: Run `npm run type-check` and report any errors. Check for `any` type usage, missing type definitions.
5. **Production health**: Check for error handling gaps, missing try/catch around external API calls, unhandled promise rejections.

USE THESE TOOLS: Bash (npm audit, type-check), Grep (security patterns, dead code), Glob (file structure), Read (specific files)

DO NOT fix anything. Report findings only.

{JSON output instructions below}

Read the full file on GitHub · 217 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 217 lines · 40 tokens per session scan B f67d87e29898

Subscribe to this mod's changes

healthcheck is a skill published in the GitHub repository andrew-yangy/gru-ai (153 stars, last pushed 6mo ago), licensed MIT. It adds 40 tokens to every session and 2,196 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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